Abstract
A universal concern impacting individuals, businesses, and governments has arisen with regard to the security of the networked infrastructure. Networked network attacks have considerably increased, and attackers’ strategies are developing. Another approach is to stop these attacks from happening in the first place. There are other approaches for constructing IDSs. One successful technique is to use machine learning. When discriminative and representative qualities are utilized, an IDS will experience an exponential rise in performance. Two unique strategies are used for lowering the number of features. First, an AE reduces the dimensionality of features and, second, Principal Component Analysis is used to produce new, higher-order features. Classifiers like Bayesian Network, Support Vector Machines, and Random Forest are produced using the two classifier generation strategies. Binary classification trials help increase the Accuracy, Detection Rate, F-measure, and False Alarm Rate. As a result of this research, data characteristics from NSL-KDD will be removed from 41 to 10 from the dataset, resulting in a 99.6% accuracy rating in the multi-class and binary modes.
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Srikanth Yadav, M., Kalpana, R. (2022). Effective Dimensionality Reduction Techniques for Network Intrusion Detection System Based on Deep Learning. In: Jacob, I.J., Kolandapalayam Shanmugam, S., Bestak, R. (eds) Data Intelligence and Cognitive Informatics. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-6460-1_39
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